WO2021159833A1 - 一种核酸质谱数值处理方法 - Google Patents

一种核酸质谱数值处理方法 Download PDF

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WO2021159833A1
WO2021159833A1 PCT/CN2020/134810 CN2020134810W WO2021159833A1 WO 2021159833 A1 WO2021159833 A1 WO 2021159833A1 CN 2020134810 W CN2020134810 W CN 2020134810W WO 2021159833 A1 WO2021159833 A1 WO 2021159833A1
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peak
mass spectrum
mass
spectrum
fitting
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PCT/CN2020/134810
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French (fr)
Chinese (zh)
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树建伟
相双红
汪松炯
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浙江迪谱诊断技术有限公司
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Priority to EP20918621.2A priority Critical patent/EP4016379B1/en
Priority to US17/771,216 priority patent/US20220383979A1/en
Priority to JP2022535644A priority patent/JP7456665B2/ja
Publication of WO2021159833A1 publication Critical patent/WO2021159833A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J49/00—Particle spectrometers or separator tubes
    • H01J49/0027—Methods for using particle spectrometers
    • H01J49/0036—Step by step routines describing the handling of the data generated during a measurement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

Definitions

  • the invention belongs to the technical field of nucleic acid mass spectrometry, and specifically relates to a numerical processing method for nucleic acid mass spectrometry.
  • Mass spectrometry technology has the advantages of rapidness, accuracy, and high sensitivity, and has been widely used in biological analysis in recent years.
  • nucleic acid plays a vital role in the growth, development, reproduction, heredity and mutation of organisms and other major life phenomena.
  • Modern biotechnology has discovered that most physiological or disease traits are manifested by a series of gene regulation existing in nucleic acid sequences. Therefore, for nucleic acids, accurate nucleotide detection is particularly important.
  • Mass spectrometry numerical processing is used as a nuclear It is self-evident that it is an indispensable part before glycidic acid detection. At present, there are problems such as low data collection conversion rate and uneven data in similar methods. These problems seriously affect the results of nucleotide detection, and further research is needed in this regard.
  • the purpose of the present invention is to solve the problems of low conversion rate and uneven data in the mass spectrometry data acquisition process, and to provide a nucleic acid mass spectrometry numerical processing method to extract reliable feature values before gene analysis, which is a way to improve the prior art
  • the limitation is a numerical processing method for nucleic acid mass spectrometry aimed at improving the accuracy of nucleotide detection.
  • a numerical processing method for nucleic acid mass spectrometry including the following steps:
  • Step S1 Recalibrate a single mass spectrum. For each detection point of the sample, obtain several mass spectra corresponding to different positions of the detection point. Each mass spectrum needs to use a special set of peaks with the expected mass-to-charge ratio, that is, anchor peaks. Recalibration;
  • Step S2 mass spectrum synthesis, based on step S1, combining several mass spectra corresponding to the same position of the detection point into a single mass spectrum of the detection point;
  • Step S3 Wavelet filtering, on the basis of step S2, eliminate high-frequency noise and baseline through a wavelet-based digital filter;
  • Step S4 Peak feature value extraction. On the basis of step S3, peak fitting is performed, and the peak height, peak width, peak area, mass offset, and signal-to-noise ratio are obtained based on the fitting curve of the mass spectrum.
  • the step of recalibrating a single mass spectrum includes:
  • Step S11 Candidate reference peak selection, select a set of reference peaks from all possible expected peaks according to the following criteria: first, the peak must be within the quality range of a specific interval; second, there is no adjacent reference peak within the quality range of the specific interval ;
  • Step S12 Peak location, a weight matrix convolution filter with a width of 9 is applied to the mass spectrum, the matrix is preferably: (-4,0,1,2,2,2,1,0,-4), for the mass spectrum
  • the intensity value after applying this filtering is equal to the weighted sum of the surrounding 9 values, expressed by the following formula:
  • the total mass spectrum is decomposed into specific point intervals. For each interval, local noise is identified, and the local noise with an intensity greater than or equal to four times the local noise and greater than or equal to the global minimum is identified as a candidate peak.
  • the minimum value is preferably 0.01 *Maximum local maximum;
  • Step S13 mass spectrum peak fitting
  • Step S14 Final anchor peak selection.
  • For the detected peak list first find the cut-off SNR, that is, the minimum SNR.
  • the detected peak matches the candidate reference peak list, and only select the quality within the specific range of the candidate reference peak and the SNR is higher than the cut-off Those peaks of SNR;
  • Step S15 Recalibrate, combine the obtained anchor peaks and their expected masses, and calculate the calibration coefficients using a nonlinear fitting method.
  • the mapping function between the mass spectrometer and m/z (mass-to-charge ratio) is the Bruker function .
  • the function form is
  • step S13 the specific steps of peak fitting in step S13 include:
  • Step S131 Determine the expected line width.
  • Step S132 The area of the expected signal is shielded within the interval of NN expected line widths, and NN is preferably 4.
  • Step S133 The implicit baseline is calculated as the average value of the mass spectrum intensity y i in the MM ⁇ m interval, where ⁇ m is the smallest estimated line width in this interval, and MM is preferably 80. In the shielding area of this interval, linear Interpolation provides the value of y i.
  • Step S134 Calculate the noise level as the running effective value (RMS) of (signal-baseline).
  • Step S135 shielding points, the points in the peak area whose SNR (SNR is calculated as the ratio of peak height to noise) greater than a given value and noise greater than a given value will be further shielded.
  • Step S136 The specific estimated line width of each peak is determined as the fitting area.
  • the Levenberg-Marquardt algorithm is used to fit a single Gaussian peak, and the specified parameters are found to minimize the tuning function.
  • the specific steps of determining the expected line width include:
  • ⁇ e L A +L B ⁇ M, where L A and L B are default parameters, and M is a given peak value (Da).
  • the specific steps of the tuning function include:
  • the sum is the sum of all ⁇ y i ,m i ⁇ from the specified interval
  • H f is the fitting height above the baseline corresponding to the point M f
  • the parameters M f and ⁇ f indicate the fitting quality
  • the combined line width, ⁇ i is given as a certain parameter according to the conditions.
  • the specific step of extracting feature values in the step S4 includes:
  • Step S41 peak fitting, the same as step S13;
  • Step S42 Record the following characteristics:
  • V A/SNR, area variance
  • the nucleic acid mass spectrometry numerical processing method of the present invention extracts reliable feature values before gene analysis. It is a nucleic acid mass spectrometry numerical processing aiming at improving the limitations of the prior art and improving the accuracy of nucleotide detection. Method; 2. Improve the credibility of nucleic acid mass spectrometry data collection.
  • Figure 3 Comparison chart before and after peak fitting.
  • the present invention is a numerical processing method for nucleic acid mass spectrometry, which includes the following steps:
  • Step S1 Recalibration of a single mass spectrum.
  • the mass spectrometer needs to be recalibrated.
  • the recalibration process is achieved by matching a set of specially identified peaks called anchor peaks to their expected masses, and follows the following steps:
  • Step S11 Selection of candidate reference peaks.
  • a set of clean reference peaks is selected from all possible expected peaks. The criteria are as follows:
  • the peak value must be within the mass range of 4000Da and 9000Da.
  • the peak has no adjacent reference peaks within the mass range defined by mass +/- resolution.
  • Step S12 Peak location, a weight matrix convolution filter with a width of 9 is applied to the mass spectrum, the matrix is preferably: (-4,0,1,2,2,2,1,0,-4), for the mass spectrum At a fixed point, the intensity value after applying this filtering is equal to the weighted sum of the surrounding 9 values, expressed by the following formula:
  • the peaks with adjacent candidate peaks in a certain range, SNR (the ratio of the filtered intensity value to the local noise) ⁇ 2, and the quality value outside the pre-specified candidate reference peak range will be cleared. Finally, the peak index is adjusted based on the original intensity. Refer to Figure 1 and Figure 2 for the mass spectra before and after applying the filter.
  • Step S13 mass spectrum peak fitting, see Figure 3, the specific implementation steps are as follows:
  • Step S131 Determine the expected line width, which is determined by the following formula:
  • ⁇ e L A +L B ⁇ M, where L A and L B are default parameters (the default values are 2.5 and 0.0005 respectively), and M is the given peak value (Da).
  • Step S132 The area of the expected signal is shielded within the interval of NN expected line widths, and NN is preferably 4.
  • Step S133 The implicit baseline is calculated as the average value of the mass spectrum intensity y i in the MM ⁇ m interval, where ⁇ m is the smallest estimated line width in this interval, and MM is preferably 80. In the shielding area of this interval, linear Interpolation provides the value of y i.
  • Step S134 Calculate the noise level as the running effective value (RMS) of (signal-baseline).
  • Step S135 The points in the peak area where the SNR (SNR is calculated as the ratio of peak height to noise) greater than 5 and noise greater than 1 will be further shielded.
  • SNR SNR is calculated as the ratio of peak height to noise
  • Step S136 The 4 estimated line widths of each peak are determined as the fitting area.
  • the sum is the sum of all ⁇ y i ,m i ⁇ from the specified interval, and H f is the fitting height above the baseline corresponding to the point M f.
  • ⁇ i is set equal to 1
  • ⁇ i is set to 0.2 or 0.4.
  • Step S14 Final final anchor peak selection.
  • the detected peak list first find the cut-off SNR (ie the minimum SNR), the detected peak matches the candidate reference peak list, and only select the quality within +/-25Da of the candidate reference peak And the SNR is higher than those peaks of the cut-off SNR.
  • the cut-off SNR ie the minimum SNR
  • Step S15 Recalibrate, combine the obtained anchored peaks and their expected masses, and calculate the calibration coefficients using the method of nonlinear fitting.
  • the mapping function between the mass spectrometer and m/z (mass-to-charge ratio) is the Bruker function .
  • the function form is
  • Step S2 mass spectrum synthesis, based on step S1, a number of mass spectra at different positions of the corresponding points are summarized into a unique mass spectrum of the detection point.
  • the method of synthesizing multiple mass spectra is "self-weighted average", which can be described by the following equation:
  • n is the number of mass spectra
  • I ij the intensity of mass i from the jth mass spectrum.
  • the best mass spectrum with the most anchored peaks is selected from the mass spectra. Initialize the added spectrum with the best spectrum. Only when the calibration coefficient and the calibration coefficient of the best spectrum meet the conditions (A should vary within 1%; B should vary within 10%; C should vary within 20Da), the absolute intensity or square of the mass spectrum and another mass spectrum Intensity summation.
  • Step S3 Wavelet filtering. Wavelet-based filtering is completed on the synthesized mass spectrum to eliminate high-frequency noise and baseline. Then another round of recalibration is performed on the filtered mass spectrum. After this round of recalibration, assign the new ABC coefficients to the synthesized mass spectrum and adjust the m/z value accordingly.
  • Step S4 Peak feature value extraction, see Figure 3.
  • the fitting process follows the following steps:
  • Step S41 Peak fitting, the step is the same as S13;
  • Step S42 Record the following characteristics after fitting successfully:
  • V A/SNR, area variance
  • the nucleic acid mass spectrum numerical processing method of the present invention extracts reliable characteristic values before gene analysis, and is a nucleic acid mass spectrum numerical value aimed at improving the limitations of the prior art and increasing the accuracy of nucleotide detection; and improving the possibility of nucleic acid mass spectrometry data collection Reliability.

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PCT/CN2020/134810 2020-02-10 2020-12-09 一种核酸质谱数值处理方法 WO2021159833A1 (zh)

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